Abstract:
An increasingly number of innovative applications relies on the processing of huge spatial data. In this regard, we are interested in contexts where deluges of these spatial data are continuously produced, such as mobile phone infrastructures, aircraft anti-collision systems, but also contexts related to virtual and simulated worlds, like those pertaining to either Massively Multiplayer Online Games (MMOG), or behavioral simulations (also known as agent-based simulations), where agents may act the behavior decisions of other agents within a given range.
In many of the application scenarios deriving from the contexts mentioned above, we have multitudes of moving objects that routinely and frequently report their new positions and, at the same time, may search for other objects in their interaction range.
In our work we explored the usage of modern GPUs in order to speed up the management of these scenarios through an approach we called "iterated spatial joins". Nowadays GPUs can be programmed for solving general scientific problems; however, despite their computational power the architecture of these devices poses some important limitations which must be taken into account to achieve significant advantages through their use.
We will therefore show what the main problems which had to be tackled were and the solutions adopted in the work, the experimental results obtained so far and the open problems which still have to be solved for achieving optimal performances when processing skewed spatial objects distributions.

Joint work with: Salvatore Orlando and Claudio Silvestri

Host: Ira Assent